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Evaluation of Objective Quality Measures
for Speech Enhancement
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提升复杂系统的定量决策支持,将成本作为独立变量(CAIV)寻求“最佳”点设计,是一个约束的非线性优化问题,其目标函数是最优有效性度量(MOE)表示,由基于性能的成本模型、二阶约束MOEs、系统性能指标的界限(MOPs)构成。算法采用的是同时扰动随机逼近方法(SPSA)。附件中是二阶约束MOEs模型的仿真程序。附:仿真流程图-Ascend the quantitative decision support of complex systems, will cost as an independen
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One of the major drawbacks of the block-based DCT compression methods is that they may result in visible artifacts at block boundaries due to coarse quantization of the coefficients. -Simulation results show that the proposed algorithm significantly
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多种语音信号质量评判工具,包括pesq、LLR、坂仓距离等。-This folder contains implementations of objective measures,include:PESQ measure,Likelihood-ratio measure,takura-Saito measure and so on.
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This function implements the composite objective measure
proposed in [1]. It returns three values: The predicted rating of
overall quality (Covl), the rating of speech distortion (Csig) and
the rating of background distortion (Cbak). The rat
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Commonly used uation measures including Recall, Precision, F-Measure and Rand Accuracy are
biased and should not be used without clear understanding of the biases, and corresponding identification of chance
or base case levels of the statistic.
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Multisensor image fusion has its effective utilization for surveillance.
In this paper, we utilize a pulse coupled neural network method to merge images
different sensors, in order to enhance visualization for surveillance. On the
basis of sta
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The objective of this chapter is to define the problem of image denoising and describes about the condition in which image denoising is important. Also discuss about performance measures to evaluate image denoising results. Image processing is an imp
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The objective of this chapter is to define the problem of image denoising and describes about the condition in which image denoising is important. Also discuss about performance measures to evaluate image denoising results. Image processing is an imp
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